Resum:

Recognition and tracking of multiple objects in video sequences is one of the main challenges in computer vision that currently deserves a lot of attention from researchers. Almost all the reported approaches are very application-dependent and there is a lack of a general methodology for dynamic object recognition and tracking that can be instantiated in particular cases. In this thesis, the work is oriented towards the definition and development of such a methodology which integrates object recognition and tracking from a general perspective using a probabilistic framework called PIORT (probabilistic integrated object recognition and tracking framework). It include some modules for which a variety of techniques and methods can be applied. Some of them are well-known but other methods have been designed, implemented and tested during the development of this thesis.

The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB colour features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods. All these methods have been tested experimentally in several test video sequences taken with still and moving cameras and including full and partial occlusions of the tracked object in indoor and outdoor scenarios in a variety of cases with different levels of task complexity. This allowed the evaluation of the general methodology and the alternative methods that compose these modules.A Probabilistic Integrated Object Recognition and Tracking Framework for Video Sequences